{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "abb04358",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3835c755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.63867924,  3.76832779, -6.90939562,  5.7760455 , -8.35116146],\n",
       "       [-7.75165025, -8.27888721,  1.67739892, -2.92014507, -9.24923023],\n",
       "       [ 1.00868034, -1.35565668,  5.15742398, -6.20189702, -8.04732586]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.uniform(-10,10,size=(3,5))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "497eb1ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.63867924, 3.76832779, 6.90939562, 5.7760455 , 8.35116146],\n",
       "       [7.75165025, 8.27888721, 1.67739892, 2.92014507, 9.24923023],\n",
       "       [1.00868034, 1.35565668, 5.15742398, 6.20189702, 8.04732586]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.np.abs 绝对值\n",
    "np.abs(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6054e773",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\winlab\\AppData\\Local\\Temp/ipykernel_7732/1363791611.py:2: RuntimeWarning: invalid value encountered in sqrt\n",
      "  np.sqrt(a)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1.90753224, 1.94121812,        nan, 2.40334049,        nan],\n",
       "       [       nan,        nan, 1.29514436,        nan,        nan],\n",
       "       [1.00433079,        nan, 2.27099625,        nan,        nan]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.np.sqrt 开根\n",
    "np.sqrt(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5b0e8caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13.23998662, 14.20029436, 47.73974783, 33.36270156, 69.74189771],\n",
       "       [60.08808166, 68.53997343,  2.81366715,  8.52724722, 85.54825989],\n",
       "       [ 1.01743602,  1.83780505, 26.59902209, 38.46352661, 64.7594535 ]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.np.square 平方\n",
    "np.square(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dddb8fc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.80415598e+01, 4.33075850e+01, 9.98361004e-04, 3.22481411e+02,\n",
       "        2.36122113e-04],\n",
       "       [4.30032292e-04, 2.53819491e-04, 5.35161789e+00, 5.39258638e-02,\n",
       "        9.61856642e-05],\n",
       "       [2.74198014e+00, 2.57777960e-01, 1.73716381e+02, 2.02558442e-03,\n",
       "        3.19956388e-04]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4.np.exp 计算指数\n",
    "np.exp(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74cbd6d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\winlab\\AppData\\Local\\Temp/ipykernel_7732/2307381907.py:2: RuntimeWarning: invalid value encountered in log\n",
      "  np.log(a)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1.29162077, 1.32663135,        nan, 1.75371928,        nan],\n",
       "       [       nan,        nan, 0.51724433,        nan,        nan],\n",
       "       [0.00864288,        nan, 1.64043723,        nan,        nan]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5.np.log 求以e为底的对数\n",
    "np.log(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c0ff7afe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1., -1.,  1., -1.],\n",
       "       [-1., -1.,  1., -1., -1.],\n",
       "       [ 1., -1.,  1., -1., -1.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6.np.sign 将大于0的数变为1，等于0的变为0，小于0的变成-1\n",
    "np.sign(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ea510406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.,  4., -6.,  6., -8.],\n",
       "       [-7., -8.,  2., -2., -9.],\n",
       "       [ 2., -1.,  6., -6., -8.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7.np.ceil 朝着无穷大的方向取整，比如5.1会变成6，-6.3会变成-6\n",
    "np.ceil(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "13ed976a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3.,   3.,  -7.,   5.,  -9.],\n",
       "       [ -8.,  -9.,   1.,  -3., -10.],\n",
       "       [  1.,  -2.,   5.,  -7.,  -9.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8.np.floor 朝着负无穷方向取整 比如5.1会变成5，-6.3会变成-7\n",
    "np.floor(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "792116d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.,  4., -7.,  6., -8.],\n",
       "       [-8., -8.,  2., -3., -9.],\n",
       "       [ 1., -1.,  5., -6., -8.]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 9.np.rint或者np.round\n",
    "np.rint(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "87a3adf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.63867924  0.76832779 -0.90939562  0.7760455  -0.35116146]\n",
      " [-0.75165025 -0.27888721  0.67739892 -0.92014507 -0.24923023]\n",
      " [ 0.00868034 -0.35565668  0.15742398 -0.20189702 -0.04732586]]\n",
      "[[ 3.  3. -6.  5. -8.]\n",
      " [-7. -8.  1. -2. -9.]\n",
      " [ 1. -1.  5. -6. -8.]]\n"
     ]
    }
   ],
   "source": [
    "# 10.np.modf 将整数和小数分隔开形成两个数组\n",
    "print(np.modf(a)[0])\n",
    "print(np.modf(a)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ec802672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.47686675, -0.58650353, -0.58607836, -0.48567904, -0.87893113],\n",
       "       [-0.99476871, -0.91107766,  0.99432332, -0.21964209, -0.17464747],\n",
       "       [ 0.84612923, -0.97694659, -0.9025956 ,  0.0811988 , -0.98136716]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 11.np.sin 三角函数\n",
    "np.sin(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ab0f2a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.63867924,  4.76832779, -5.90939562,  6.7760455 , -7.35116146],\n",
       "       [-6.75165025, -7.27888721,  2.67739892, -1.92014507, -8.24923023],\n",
       "       [ 2.00868034, -0.35565668,  6.15742398, -5.20189702, -7.04732586]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+1"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "b29bc725",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b600102e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.63867924,  7.76832779, -2.90939562,  9.7760455 , -4.35116146],\n",
       "       [-4.75165025, -5.27888721,  4.67739892,  0.07985493, -6.24923023],\n",
       "       [ 5.00868034,  2.64434332,  9.15742398, -2.20189702, -4.04732586]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.add(a,np.random.randint(0,5,size=(3,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e90a15f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.63867924, 3.76832779, 5.7760455 , 1.67739892, 1.00868034,\n",
       "       5.15742398])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a[a>0]\n",
    "a[np.greater(a,0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "928815f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.63867924, 3.76832779, 1.67739892, 1.00868034])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a[(a>0)&(a<5)]\n",
    "a[np.logical_and(a>0,a<5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3b285038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6.90939562,  5.7760455 , -8.35116146, -7.75165025, -8.27888721,\n",
       "       -2.92014507, -9.24923023, -1.35565668,  5.15742398, -6.20189702,\n",
       "       -8.04732586])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[(a>5)|(a<0)]\n",
    "a[np.logical_or(a>5,a<0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a463500",
   "metadata": {},
   "source": [
    "# 聚合函数"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "b2ee4afd",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90efea27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.63867924  3.76832779 -6.90939562  5.7760455  -8.35116146]\n",
      " [-7.75165025 -8.27888721  1.67739892 -2.92014507 -9.24923023]\n",
      " [ 1.00868034 -1.35565668  5.15742398 -6.20189702 -8.04732586]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ -3.10429068,  -5.8662161 ,  -0.07457272,  -3.34599659,\n",
       "       -25.64771755])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.sum\n",
    "print(a)\n",
    "np.sum(a,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cf8ec7b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -28.45060442,   42.2931818 ,  -59.77357832,  104.6067195 ,\n",
       "       -621.59005562])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.prod 计算元素的积\n",
    "np.prod(a,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6fcb1eff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.03476356, -1.95540537, -0.02485757, -1.1153322 , -8.54923918])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.mean 计算元素的平均值\n",
    "np.mean(a,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0f173caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.86940439, 4.93650492, 5.07117946, 5.05376354, 0.51027419])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4.std 计算元素的标准差\n",
    "np.std(a,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e98eaf27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.63867924  3.76832779 -6.90939562  5.7760455  -8.35116146]\n",
      " [-7.75165025 -8.27888721  1.67739892 -2.92014507 -9.24923023]\n",
      " [ 1.00868034 -1.35565668  5.15742398 -6.20189702 -8.04732586]]\n",
      "[-7.75165025 -8.27888721 -6.90939562 -6.20189702 -9.24923023]\n",
      "[1 1 0 2 1]\n"
     ]
    }
   ],
   "source": [
    "# 5.min 求元素的最小值 argmin找出最小值的索引\n",
    "print(a)\n",
    "print(np.min(a,axis=0))\n",
    "print(np.argmin(a,axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6de8727f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.920145067491622"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6.median 计算元素的中位数\n",
    "np.median(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c92684a5",
   "metadata": {},
   "source": [
    "%time 将会给出当前行的代码运行一次所花费的时间   \n",
    "%%time 将会给出cell的代码运行一次所花费的时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4934e0f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 98.4 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<timed eval>:1: RuntimeWarning: overflow encountered in long_scalars\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1783293664"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 验证python内置的sum函数效率没有np.sum高\n",
    "%time sum(np.arange(1000000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "970b0e42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2.99 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1783293664"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time np.sum(np.arange(1000000))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8ae655",
   "metadata": {},
   "source": [
    "# 布尔判断函数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9382b2e3",
   "metadata": {},
   "source": [
    "np.any 验证任何一个元素是否为真  \n",
    "np.all 验证所有元素是否为真"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a747c59b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.63867924  3.76832779 -6.90939562  5.7760455  -8.35116146]\n",
      " [-7.75165025 -8.27888721  1.67739892 -2.92014507 -9.24923023]\n",
      " [ 1.00868034 -1.35565668  5.15742398 -6.20189702 -8.04732586]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(a)\n",
    "np.all(a==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fd585cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.any(a>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cd09366f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(a>0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b2de56",
   "metadata": {},
   "source": [
    "# 排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1d93800f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-7.75165025, -8.27888721, -6.90939562, -6.20189702, -9.24923023],\n",
       "       [ 1.00868034, -1.35565668,  1.67739892, -2.92014507, -8.35116146],\n",
       "       [ 3.63867924,  3.76832779,  5.15742398,  5.7760455 , -8.04732586]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.sort按指定轴进行排序\n",
    "np.sort(a,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "040b4950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 2, 2, 0, 6],\n",
       "       [5, 1, 3, 7, 4],\n",
       "       [4, 2, 6, 9, 8]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.random.randint(0,10,size=(3,5))\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e1eda624",
   "metadata": {},
   "outputs": [],
   "source": [
    "b.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "92f265aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 2, 2, 6],\n",
       "       [1, 3, 4, 5, 7],\n",
       "       [2, 4, 6, 8, 9]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9f4b8aba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 0, 2, 1],\n",
       "       [2, 2, 1, 1, 0],\n",
       "       [0, 0, 2, 0, 2]], dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.argsort返回排序后的下标值\n",
    "np.argsort(a,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "462dfdb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.63867924  3.76832779 -6.90939562  5.7760455  -8.35116146]\n",
      " [-7.75165025 -8.27888721  1.67739892 -2.92014507 -9.24923023]\n",
      " [ 1.00868034 -1.35565668  5.15742398 -6.20189702 -8.04732586]]\n",
      "[[ 5.7760455   3.76832779  3.63867924 -6.90939562 -8.35116146]\n",
      " [ 1.67739892 -2.92014507 -7.75165025 -8.27888721 -9.24923023]\n",
      " [ 5.15742398  1.00868034 -1.35565668 -6.20189702 -8.04732586]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 5.7760455 ,  3.76832779,  3.63867924, -6.90939562, -8.35116146],\n",
       "       [-6.90939562,  5.7760455 ,  3.63867924,  3.76832779, -8.35116146],\n",
       "       [-6.90939562,  3.63867924,  3.76832779,  5.7760455 , -8.35116146]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.降序排序，np.sort默认采用升序排序，降序可用以下方法\n",
    "# 使用负号\n",
    "print(a)\n",
    "-np.sort(-a)\n",
    "print(-np.sort(-a))\n",
    "\n",
    "# 使用sort和argsort以及take\n",
    "indexes = np.argsort(-a)\n",
    "np.take(a,indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d52155",
   "metadata": {},
   "source": [
    "# 其它函数补充："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4393a5dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[31, 46, 89, 39, 73, 11, 65, 86, 91, 67, 67, 35, 52, 53, 79, 85,\n",
       "        93, 72, 74, 61],\n",
       "       [32, 68, 16, 53, 61, 13, 87, 57, 36, 37, 72, 37, 85,  9, 37, 69,\n",
       "        69, 76, 14, 89],\n",
       "       [79, 13, 20, 36, 88, 11, 10, 33, 17, 31, 80, 43, 79, 69,  6, 29,\n",
       "        43, 88, 25, 94]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.apply_along_axis:沿着某个轴执行指定的函数\n",
    "# 将数组每行去掉最大值和最小值后求平均值\n",
    "c = np.random.randint(0,100,size=(3,20))\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "992b533a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\winlab\\AppData\\Local\\Temp/ipykernel_7732/3081937447.py:5: RuntimeWarning: Mean of empty slice.\n",
      "  np.apply_along_axis(lambda x:x[np.logical_and(x!=x.max(),x!=x.min())].mean(),axis=0,arr=c)\n",
      "c:\\users\\winlab\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\numpy\\core\\_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([32., 46., 20., 39., 73., nan, 65., 57., 36., 37., 72., 37., 79.,\n",
       "       53., 37., 69., 69., 76., 25., 89.])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# def get_mean(x):\n",
    "#     y = x[np.logical_and(x!=x.max(),x!=x.min())].mean()\n",
    "#     return y\n",
    "# np.apply_along_axis(get_mean,axis=0,arr=c)\n",
    "np.apply_along_axis(lambda x:x[np.logical_and(x!=x.max(),x!=x.min())].mean(),axis=0,arr=c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "91d165df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.09090909, 0.18181818, 0.27272727, 0.36363636,\n",
       "       0.45454545, 0.54545455, 0.63636364, 0.72727273, 0.81818182,\n",
       "       0.90909091, 1.        ])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.linespace:用来将指定区间平均分成多少份\n",
    "np.linspace(0,1,12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0df6b1a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 0, 2, 4, 0],\n",
       "       [8, 6, 7, 5, 4],\n",
       "       [5, 0, 1, 5, 1]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.np.unique:返回数组中的唯一值\n",
    "d = np.random.randint(0,10,size=(3,5))\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a7c46d44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 4, 5, 6, 7, 8]), array([3, 2, 1, 2, 3, 1, 2, 1], dtype=int64))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(d)\n",
    "np.unique(d,return_counts=True)"
   ]
  }
 ],
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